Immunogenic arenavirus vector SIV vaccine reduces setpoint viral load in SIV-challenged rhesus monkeys.

HIV affects more than 38 million people worldwide. Although HIV can be effectively treated by lifelong combination antiretroviral therapy, only a handful of patients have been cured. Therapeutic vaccines that induce robust de novo immune responses targeting HIV proteins and latent reservoirs will likely be integral for functional HIV cure. Our study shows that immunization of naïve rhesus macaques with arenavirus-derived vaccine vectors encoding simian immunodeficiency virus (SIVSME543 Gag, Env, and Pol) immunogens is safe, immunogenic, and efficacious. Immunization induced robust SIV-specific CD8+ and CD4+ T-cell responses with expanded cellular breadth, polyfunctionality, and Env-binding antibodies with antibody-dependent cellular cytotoxicity. Vaccinated animals had significant reductions in median SIV viral load (1.45-log10 copies/mL) after SIVMAC251 challenge compared with placebo. Peak viral control correlated with the breadth of Gag-specific T cells and tier 1 neutralizing antibodies. These results support clinical investigation of arenavirus-based vectors as a central component of therapeutic vaccination for HIV cure.

Intrahepatic quantification of HBV antigens in chronic hepatitis B reveals heterogeneity and treatment-mediated reductions in HBV core-positive cells.

Background & Aims: Patterns of liver HBV antigen expression have been described but not quantified at single-cell resolution. We applied quantitative techniques to liver biopsies from individuals with chronic hepatitis B and evaluated sampling heterogeneity, effects of disease stage, and nucleos(t)ide (NUC) treatment, and correlations between liver and peripheral viral biomarkers. Methods: Hepatocytes positive for HBV core and HBsAg were quantified using a novel four-plex immunofluorescence assay and image analysis. Biopsies were analysed from HBeAg-positive (n = 39) and HBeAg-negative (n = 75) participants before and after NUC treatment. To evaluate sampling effects, duplicate biopsies collected at the same time point were compared. Serum or plasma samples were evaluated for levels of HBV DNA, HBsAg, hepatitis B core-related antigen (HBcrAg), and HBV RNA. Results: Diffusely distributed individual HBV core+ cells and foci of HBsAg+ cells were the most common staining patterns. Hepatocytes positive for both HBV core and HBsAg were rare. Paired biopsies revealed large local variation in HBV staining within participants, which was confirmed in a large liver resection. NUC treatment was associated with a >100-fold lower median frequency of HBV core+ cells in HBeAg-positive and HBeAg-negative participants, whereas reductions in HBsAg+ cells were not statistically significant. The frequency of HBV core+ hepatocytes was lower in HBeAg-negative participants than in HBeAg-positive participants at all time points evaluated. Total HBV+ hepatocyte burden correlated with HBcrAg, HBV DNA, and HBV RNA only in baseline HBeAg-positive samples. Conclusions: Reductions in HBV core+ hepatocytes were associated with HBeAg-negative status and NUC treatment. Variation in HBV positivity within individual livers was extensive. Correlations between the liver and the periphery were found only between biomarkers likely indicative of cccDNA (HBV core+ and HBcrAg, HBV DNA, and RNA). Impact and Implications: HBV infects liver hepatocyte cells, and its genome can exist in two forms that express different sets of viral proteins: a circular genome called cccDNA that can express all viral proteins, including the HBV core and HBsAg proteins, or a linear fragment that inserts into the host genome typically to express HBsAg, but not HBV core. We used new techniques to determine the percentage of hepatocytes expressing the HBV core and HBsAg proteins in a large set of liver biopsies. We find that abundance and patterns of expression differ across patient groups and even within a single liver and that NUC treatment greatly reduces the number of core-expressing hepatocytes.

Novel, Selective Inhibitors of USP7 Uncover Multiple Mechanisms of Antitumor Activity In Vitro and In Vivo.

The deubiquitinase USP7 regulates the levels of multiple proteins with roles in cancer progression and immune response. Thus, USP7 inhibition may decrease oncogene function, increase tumor suppressor function, and sensitize tumors to DNA-damaging agents. We have discovered a novel chemical series that potently and selectively inhibits USP7 in biochemical and cellular assays. Our inhibitors reduce the viability of multiple TP53 wild-type cell lines, including several hematologic cancer and MYCN-amplified neuroblastoma cell lines, as well as a subset of TP53-mutant cell lines in vitro Our work suggests that USP7 inhibitors upregulate transcription of genes normally silenced by the epigenetic repressor complex, polycomb repressive complex 2 (PRC2), and potentiate the activity of PIM and PI3K inhibitors as well as DNA-damaging agents. Furthermore, oral administration of USP7 inhibitors inhibits MM.1S (multiple myeloma; TP53 wild type) and H526 (small cell lung cancer; TP53 mutant) tumor growth in vivo Our work confirms that USP7 is a promising, pharmacologically tractable target for the treatment of cancer.

Early role for IL-6 signalling during generation of induced pluripotent stem cells revealed by heterokaryon RNA-Seq.

Molecular insights into somatic cell reprogramming to induced pluripotent stem cells (iPS) would aid regenerative medicine, but are difficult to elucidate in iPS because of their heterogeneity, as relatively few cells undergo reprogramming (0.1-1%; refs , ). To identify early acting regulators, we capitalized on non-dividing heterokaryons (mouse embryonic stem cells fused to human fibroblasts), in which reprogramming towards pluripotency is efficient and rapid, enabling the identification of transient regulators required at the onset. We used bi-species transcriptome-wide RNA-seq to quantify transcriptional changes in the human somatic nucleus during reprogramming towards pluripotency in heterokaryons. During heterokaryon reprogramming, the cytokine interleukin 6 (IL6), which is not detectable at significant levels in embryonic stem cells, was induced 50-fold. A 4-day culture with IL6 at the onset of iPS reprogramming replaced stably transduced oncogenic c-Myc such that transduction of only Oct4, Klf4 and Sox2 was required. IL6 also activated another Jak/Stat target, the serine/threonine kinase gene Pim1, which accounted for the IL6-mediated twofold increase in iPS frequency. In contrast, LIF, another induced GP130 ligand, failed to increase iPS frequency or activate c-Myc or Pim1, thereby revealing a differential role for the two Jak/Stat inducers in iPS generation. These findings demonstrate the power of heterokaryon bi-species global RNA-seq to identify early acting regulators of reprogramming, for example, extrinsic replacements for stably transduced transcription factors such as the potent oncogene c-Myc.

Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets.

Current work in elucidating relationships between diseases has largely been based on pre-existing knowledge of disease genes. Consequently, these studies are limited in their discovery of new and unknown disease relationships. We present the first quantitative framework to compare and contrast diseases by an integrated analysis of disease-related mRNA expression data and the human protein interaction network. We identified 4,620 functional modules in the human protein network and provided a quantitative metric to record their responses in 54 diseases leading to 138 significant similarities between diseases. Fourteen of the significant disease correlations also shared common drugs, supporting the hypothesis that similar diseases can be treated by the same drugs, allowing us to make predictions for new uses of existing drugs. Finally, we also identified 59 modules that were dysregulated in at least half of the diseases, representing a common disease-state "signature". These modules were significantly enriched for genes that are known to be drug targets. Interestingly, drugs known to target these genes/proteins are already known to treat significantly more diseases than drugs targeting other genes/proteins, highlighting the importance of these core modules as prime therapeutic opportunities.

Evolutionarily conserved herpesviral protein interaction networks.

Herpesviruses constitute a family of large DNA viruses widely spread in vertebrates and causing a variety of different diseases. They possess dsDNA genomes ranging from 120 to 240 kbp encoding between 70 to 170 open reading frames. We previously reported the protein interaction networks of two herpesviruses, varicella-zoster virus (VZV) and Kaposi's sarcoma-associated herpesvirus (KSHV). In this study, we systematically tested three additional herpesvirus species, herpes simplex virus 1 (HSV-1), murine cytomegalovirus and Epstein-Barr virus, for protein interactions in order to be able to perform a comparative analysis of all three herpesvirus subfamilies. We identified 735 interactions by genome-wide yeast-two-hybrid screens (Y2H), and, together with the interactomes of VZV and KSHV, included a total of 1,007 intraviral protein interactions in the analysis. Whereas a large number of interactions have not been reported previously, we were able to identify a core set of highly conserved protein interactions, like the interaction between HSV-1 UL33 with the nuclear egress proteins UL31/UL34. Interactions were conserved between orthologous proteins despite generally low sequence similarity, suggesting that function may be more conserved than sequence. By combining interactomes of different species we were able to systematically address the low coverage of the Y2H system and to extract biologically relevant interactions which were not evident from single species.

eQED: an efficient method for interpreting eQTL associations using protein networks.

Analysis of expression quantitative trait loci (eQTLs) is an emerging technique in which individuals are genotyped across a panel of genetic markers and, simultaneously, phenotyped using DNA microarrays. Because of the spacing of markers and linkage disequilibrium, each marker may be near many genes making it difficult to finely map which of these genes are the causal factors responsible for the observed changes in the downstream expression. To address this challenge, we present an efficient method for prioritizing candidate genes at a locus. This approach, called 'eQTL electrical diagrams' (eQED), integrates eQTLs with protein interaction networks by modeling the two data sets as a wiring diagram of current sources and resistors. eQED achieved a 79% accuracy in recovering a reference set of regulator-target pairs in yeast, which is significantly higher than the performance of three competing methods. eQED also annotates 368 protein-protein interactions with their directionality of information flow with an accuracy of approximately 75%.

A direct comparison of protein interaction confidence assignment schemes.

BACKGROUND: Recent technological advances have enabled high-throughput measurements of protein-protein interactions in the cell, producing large protein interaction networks for various species at an ever-growing pace. However, common technologies like yeast two-hybrid may experience high rates of false positive detection. To combat false positive discoveries, a number of different methods have been recently developed that associate confidence scores with protein interactions. Here, we perform a rigorous comparative analysis and performance assessment among these different methods. RESULTS: We measure the extent to which each set of confidence scores correlates with similarity of the interacting proteins in terms of function, expression, pattern of sequence conservation, and homology to interacting proteins in other species. We also employ a new metric, the Signal-to-Noise Ratio of protein complexes embedded in each network, to assess the power of the different methods. Seven confidence assignment schemes, including those of Bader et al., Deane et al., Deng et al., Sharan et al., and Qi et al., are compared in this work. CONCLUSION: Although the performance of each assignment scheme varies depending on the particular metric used for assessment, we observe that Deng et al. yields the best performance overall (in three out of four viable measures). Importantly, we also find that utilizing any of the probability assignment schemes is always more beneficial than assuming all observed interactions to be true or equally likely.

The Plasmodium protein network diverges from those of other eukaryotes.

Plasmodium falciparum is the pathogen responsible for over 90% of human deaths from malaria. Therefore, it has been the focus of a considerable research initiative, involving the complete DNA sequencing of the genome, large-scale expression analyses, and protein characterization of its life-cycle stages. The Plasmodium genome sequence is relatively distant from those of most other eukaryotes, with more than 60% of the 5,334 encoded proteins lacking any notable sequence similarity to other organisms. To systematically elucidate functional relationships among these proteins, a large two-hybrid study has recently mapped a network of 2,846 interactions involving 1,312 proteins within Plasmodium. This network adds to a growing collection of available interaction maps for a number of different organisms, and raises questions about whether the divergence of Plasmodium at the sequence level is reflected in the configuration of its protein network. Here we examine the degree of conservation between the Plasmodium protein network and those of model organisms. Although we find 29 highly connected protein complexes specific to the network of the pathogen, we find very little conservation with complexes observed in other organisms (three in yeast, none in the others). Overall, the patterns of protein interaction in Plasmodium, like its genome sequence, set it apart from other species.

Conserved patterns of protein interaction in multiple species.

To elucidate cellular machinery on a global scale, we performed a multiple comparison of the recently available protein-protein interaction networks of Caenorhabditis elegans, Drosophila melanogaster, and Saccharomyces cerevisiae. This comparison integrated protein interaction and sequence information to reveal 71 network regions that were conserved across all three species and many exclusive to the metazoans. We used this conservation, and found statistically significant support for 4,645 previously undescribed protein functions and 2,609 previously undescribed protein interactions. We tested 60 interaction predictions for yeast by two-hybrid analysis, confirming approximately half of these. Significantly, many of the predicted functions and interactions would not have been identified from sequence similarity alone, demonstrating that network comparisons provide essential biological information beyond what is gleaned from the genome.